ZeroSearch: Incentivize the Search Capability of LLMs without Searching

Understand

Effective information searching is essential for enhancing the reasoning and generation capabilities of large language models (LLMs).

• Recent research has explored using reinforcement learning (RL) to improve LLMs' search capabilities by interacting with live search engines in real-world environments.
• While these approaches show promising results, they face two major challenges: (1) Uncontrolled Document Quality: The quality of documents returned by search engines is often unpredictable, introducing noise and instability into the training process.
• (2) Prohibitively High API Costs: RL training requires frequent rollouts, potentially involving hundreds of thousands of search requests, which incur substantial API expenses and severely constrain scalability.